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ERIC Number: EJ1159853
Record Type: Journal
Publication Date: 2012
Pages: 20
Abstractor: As Provided
ISSN: ISSN-2155-6849
Detecting Learning Style through Biometric Technology for Mobile GBL
Mehigan, Tracey J.; Pitt, Ian
International Journal of Game-Based Learning, v2 n2 p55-74 2012
Adaptive learning systems tailor content delivery to meet specific needs of the individual for improved learning-outcomes. Learning-styles and personalities are usually determined through the completion of questionnaires. There are a number of models available for this purpose including the Myer-Briggs Model (MBTI), the Big Five Model, and the Felder Silverman Learning-Style Model (FSLSM). Most models classify the student on a number of scales. Recently, a number of studies have investigated the possibility of determining an individual's learning-style directly through their interaction patterns when using a system. Automatic learning-style detection could play a significant role in the advancement of educational gaming through personalized learning environments. Biometric devices, such as accelerometers and eye-trackers, are now available for use with mobile devices. These provide an opportunity to move toward adaptive mobile gaming environments, giving potential to track learning-styles directly through avatar movement. This paper examines mobile learning (mLearning) with an emphasis on mobile game-based environments. Adaptive learning systems are introduced. The results of studies conducted to assess the potential of biometric devices as a means of automatically detecting students'learning-styles are discussed. The potential of this research for mobile game-based learning is outlined.
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Ireland
Identifiers - Assessments and Surveys: Myers Briggs Type Indicator; Learning Style Inventory
Grant or Contract Numbers: N/A